Definition: Feature engineering is the process of selecting and transforming features from raw data to improve the performance of machine learning models.

Detailed Description:

Feature engineering is a critical step in the machine learning pipeline. By creating relevant and informative features, you can improve the accuracy and interpretability of your models.

Common feature engineering techniques include:

• Scaling: Normalizing or standardizing numerical features to a common range.
• Encoding: Converting categorical features into numerical representations.
• Binning: Grouping numerical features into bins.
• Feature creation: Creating new features by combining or transforming existing features.
• Feature selection: Choosing the most relevant features for the model.

Feature engineering is often an iterative process that requires domain knowledge and experimentation.

Related Terms:

• Machine Learning
• Data Science
• Data Preprocessing
• Feature Selection
• Feature Extraction

FAQs:

1. Why is feature engineering important?

   • Feature engineering can significantly improve the performance of machine learning models by providing relevant and informative features.

2. What are some common feature engineering techniques?

   • Scaling, encoding, binning, feature creation, and feature selection.

3. How can feature engineering be used to improve model accuracy?

   • By creating features that are more relevant to the task at hand and reducing noise in the data.